Auditory Recognition of Words-in-Noise in Normal Hearing and Mild-to-Severe Sensorineural Hearing Loss with Different Configurations
Bibliographic record
Abstract
Background and Aim: Sensorineural Hearing Loss (SNHL) reduces audibility and causes distortion, which result in difficulty with speech processing, especially in noisy environments. One of the new speech-in-noise tests is the Words-in-Noise (WIN) test. This study aimed to further investigate the Signal-to-Noise Ratio 50% (SNR-50) in subjects with mild to severe SNHL and different configurations using the Persian version of the WIN test compared to normal-hearing people. Methods: This cross-sectional study was conducted on 54 patients with SNHL aged 17– 75 years and 49 normal-hearing people aged 20–48 years. The auditory recognition in the presence of multi-talker babble noise was evaluated by the Persian version of the WIN test (named ARWIN). Results: The mean SNR-50 in the normal-hearing group was 2.56±1.2 dB, which increased significantly in subgroups with mild (10.13±4.8 dB), moderate (14.51±4.7 dB) and moderate-to-severe (16.61±4.3 dB) SNHL (p<0.001). Conclusion: People with SNHL need more SNR by nearly 4–6 times than the normal- hearing group for recognition of monosyllabic Persian words in the presence of multi-talker babble noise. Keywords: Sensorineural hearing loss; words-in-noise; auditory recognition; speech perception
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".